On GLR detection and estimation of unexpected inputs in linear discrete systems

Abstract

In this paper, we present a recursive generalized likelihood ratio (GLR) test algorithm for detecting sudden changes in linear discrete systems. We demonstrate the application of linear filtering techniques to obtain a recursive GLR algorithm so that the requirement for matrix inversions in the previously known GLR algorithms can be reduced or avoided. Furthermore, the GLR algorithm is extended to the case when the sudden change follows known linear dynamics. An adaptive filtering scheme which uses the input estimate to correct the state estimate is also presented for the time-varying input case.